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dc.contributor.author Videla, Santiago
dc.contributor.author Guziolowski, Carito
dc.contributor.author Eduati, Federica
dc.contributor.author Thiele, Sven
dc.contributor.author Gebser, Martin
dc.contributor.author Nicolas, Jacques
dc.contributor.author Saez Rodriguez, Julio
dc.contributor.author Schaub, Torsten
dc.contributor.author Siegel, Anne
dc.date.available 2017-03-22T19:31:13Z
dc.date.issued 2015-09
dc.identifier.citation Videla, Santiago; Guziolowski, Carito; Eduati, Federica; Thiele, Sven; Gebser, Martin; et al.; Learning Boolean logic models of signaling networks with ASP; Elsevier Science; Theoretical Computer Science; 599; 9-2015; 79-101
dc.identifier.issn 0304-3975
dc.identifier.uri http://hdl.handle.net/11336/14203
dc.description.abstract Boolean networks provide a simple yet powerful qualitative modeling approach in systems biology. However, manual identification of logic rules underlying the system being studied is in most cases out of reach. Therefore, automated inference of Boolean logical networks from experimental data is a fundamental question in this field. This paper addresses the problem consisting of learning from a prior knowledge network describing causal interactions and phosphorylation activities at a pseudo-steady state, Boolean logic models of immediate-early response in signaling transduction networks. The underlying optimization problem has been so far addressed through mathematical programming approaches and the use of dedicated genetic algorithms. In a recent work we have shown severe limitations of stochastic approaches in this domain and proposed to use Answer Set Programming (ASP), considering a simpler problem setting. Herein, we extend our previous work in order to consider more realistic biological conditions including numerical datasets, the presence of feedback-loops in the prior knowledge networkand the necessity of multi-objective optimization. In order to cope with such extensions, we propose several discretization schemes and elaborate upon our previous ASP encoding. Towards real-world biological data, we evaluate the performance of our approach over in siliconumerical datasets based on a real and large-scale prior knowledge network. The correctness of our encoding and discretization schemes are dealt with in Appendices A–B.
dc.format application/pdf
dc.language.iso eng
dc.publisher Elsevier Science
dc.rights info:eu-repo/semantics/restrictedAccess
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.subject Answer set programming
dc.subject Signaling transduction networks
dc.subject Boolean logic models
dc.subject Combinatorial multi-objective optimization
dc.subject.classification Ciencias de la Información y Bioinformática
dc.subject.classification Ciencias de la Computación e Información
dc.subject.classification CIENCIAS NATURALES Y EXACTAS
dc.title Learning Boolean logic models of signaling networks with ASP
dc.type info:eu-repo/semantics/article
dc.type info:ar-repo/semantics/artículo
dc.type info:eu-repo/semantics/publishedVersion
dc.date.updated 2016-12-16T17:27:04Z
dc.journal.volume 599
dc.journal.pagination 79-101
dc.journal.pais Países Bajos
dc.journal.ciudad Amsterdam
dc.description.fil Fil: Videla, Santiago. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquimicas de Buenos Aires; Argentina. CNRS. UMR; Francia. Campus de Beaulieu. Dyliss project; Francia. Universität Potsdam; Alemania
dc.description.fil Fil: Guziolowski, Carito. CNRS. École Centrale de Nantes; Francia
dc.description.fil Fil: Eduati, Federica. European Bioinformatics Institute. European Molecular Biology Laboratory; Reino Unido
dc.description.fil Fil: Thiele, Sven. CNRS. UMR; Francia. Campus de Beaulieu. Dyliss project; Francia
dc.description.fil Fil: Gebser, Martin. Universität Potsdam; Alemania
dc.description.fil Fil: Nicolas, Jacques. CNRS. UMR; Francia. Campus de Beaulieu. Dyliss project; Francia
dc.description.fil Fil: Saez Rodriguez, Julio. European Bioinformatics Institute. European Molecular Biology Laboratory; Reino Unido
dc.description.fil Fil: Schaub, Torsten. Universität Potsdam; Alemania
dc.description.fil Fil: Siegel, Anne. CNRS. UMR; Francia. Campus de Beaulieu. Dyliss project; Francia
dc.journal.title Theoretical Computer Science
dc.relation.alternativeid info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0304397514004587
dc.relation.alternativeid info:eu-repo/semantics/altIdentifier/url/http://dx.doi.org/10.1016/j.tcs.2014.06.022


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info:eu-repo/semantics/restrictedAccess Excepto donde se diga explícitamente, este item se publica bajo la siguiente descripción: Atribución-NoComercial-SinDerivadas 2.5 Argentina (CC BY-NC-ND 2.5 AR)